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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code>.MiniBatchDictionaryLearning</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-decomposition-minibatchdictionarylearning">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.decomposition.MiniBatchDictionaryLearning</span></code></a></li>
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  <div class="section" id="sklearn-decomposition-minibatchdictionarylearning">
<h1><a class="reference internal" href="../classes.html#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a>.MiniBatchDictionaryLearning<a class="headerlink" href="#sklearn-decomposition-minibatchdictionarylearning" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.decomposition.</code><code class="sig-name descname">MiniBatchDictionaryLearning</code><span class="sig-paren">(</span><em class="sig-param">n_components=None</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">n_iter=1000</em>, <em class="sig-param">fit_algorithm='lars'</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">batch_size=3</em>, <em class="sig-param">shuffle=True</em>, <em class="sig-param">dict_init=None</em>, <em class="sig-param">transform_algorithm='omp'</em>, <em class="sig-param">transform_n_nonzero_coefs=None</em>, <em class="sig-param">transform_alpha=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">split_sign=False</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">positive_code=False</em>, <em class="sig-param">positive_dict=False</em>, <em class="sig-param">transform_max_iter=1000</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1244"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning" title="Permalink to this definition">¶</a></dt>
<dd><p>Mini-batch dictionary learning</p>
<p>Finds a dictionary (a set of atoms) that can best be used to represent data
using a sparse code.</p>
<p>Solves the optimization problem:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">U</span><span class="o">^*</span><span class="p">,</span><span class="n">V</span><span class="o">^*</span><span class="p">)</span> <span class="o">=</span> <span class="n">argmin</span> <span class="mf">0.5</span> <span class="o">||</span> <span class="n">Y</span> <span class="o">-</span> <span class="n">U</span> <span class="n">V</span> <span class="o">||</span><span class="n">_2</span><span class="o">^</span><span class="mi">2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span> <span class="n">U</span> <span class="o">||</span><span class="n">_1</span>
             <span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">V</span><span class="p">)</span>
             <span class="k">with</span> <span class="o">||</span> <span class="n">V_k</span> <span class="o">||</span><span class="n">_2</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">for</span> <span class="nb">all</span>  <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">n_components</span>
</pre></div>
</div>
<p>Read more in the <a class="reference internal" href="../decomposition.html#dictionarylearning"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_components</strong><span class="classifier">int,</span></dt><dd><p>number of dictionary elements to extract</p>
</dd>
<dt><strong>alpha</strong><span class="classifier">float,</span></dt><dd><p>sparsity controlling parameter</p>
</dd>
<dt><strong>n_iter</strong><span class="classifier">int,</span></dt><dd><p>total number of iterations to perform</p>
</dd>
<dt><strong>fit_algorithm</strong><span class="classifier">{‘lars’, ‘cd’}</span></dt><dd><p>lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>Number of parallel jobs to run.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>batch_size</strong><span class="classifier">int,</span></dt><dd><p>number of samples in each mini-batch</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool,</span></dt><dd><p>whether to shuffle the samples before forming batches</p>
</dd>
<dt><strong>dict_init</strong><span class="classifier">array of shape (n_components, n_features),</span></dt><dd><p>initial value of the dictionary for warm restart scenarios</p>
</dd>
<dt><strong>transform_algorithm</strong><span class="classifier">{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’,     ‘threshold’}</span></dt><dd><p>Algorithm used to transform the data.
lars: uses the least angle regression method (linear_model.lars_path)
lasso_lars: uses Lars to compute the Lasso solution
lasso_cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). lasso_lars will be faster if
the estimated components are sparse.
omp: uses orthogonal matching pursuit to estimate the sparse solution
threshold: squashes to zero all coefficients less than alpha from
the projection dictionary * X’</p>
</dd>
<dt><strong>transform_n_nonzero_coefs</strong><span class="classifier">int, <code class="docutils literal notranslate"><span class="pre">0.1</span> <span class="pre">*</span> <span class="pre">n_features</span></code> by default</span></dt><dd><p>Number of nonzero coefficients to target in each column of the
solution. This is only used by <code class="docutils literal notranslate"><span class="pre">algorithm='lars'</span></code> and <code class="docutils literal notranslate"><span class="pre">algorithm='omp'</span></code>
and is overridden by <code class="docutils literal notranslate"><span class="pre">alpha</span></code> in the <code class="docutils literal notranslate"><span class="pre">omp</span></code> case.</p>
</dd>
<dt><strong>transform_alpha</strong><span class="classifier">float, 1. by default</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_lars'</span></code> or <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_cd'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the
penalty applied to the L1 norm.
If <code class="docutils literal notranslate"><span class="pre">algorithm='threshold'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the absolute value of the
threshold below which coefficients will be squashed to zero.
If <code class="docutils literal notranslate"><span class="pre">algorithm='omp'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the tolerance parameter: the value of
the reconstruction error targeted. In this case, it overrides
<code class="docutils literal notranslate"><span class="pre">n_nonzero_coefs</span></code>.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, optional (default: False)</span></dt><dd><p>To control the verbosity of the procedure.</p>
</dd>
<dt><strong>split_sign</strong><span class="classifier">bool, False by default</span></dt><dd><p>Whether to split the sparse feature vector into the concatenation of
its negative part and its positive part. This can improve the
performance of downstream classifiers.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
<dt><strong>positive_code</strong><span class="classifier">bool</span></dt><dd><p>Whether to enforce positivity when finding the code.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
<dt><strong>positive_dict</strong><span class="classifier">bool</span></dt><dd><p>Whether to enforce positivity when finding the dictionary.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
<dt><strong>transform_max_iter</strong><span class="classifier">int, optional (default=1000)</span></dt><dd><p>Maximum number of iterations to perform if <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_cd'</span></code> or
<code class="docutils literal notranslate"><span class="pre">lasso_lars</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>components_</strong><span class="classifier">array, [n_components, n_features]</span></dt><dd><p>components extracted from the data</p>
</dd>
<dt><strong>inner_stats_</strong><span class="classifier">tuple of (A, B) ndarrays</span></dt><dd><p>Internal sufficient statistics that are kept by the algorithm.
Keeping them is useful in online settings, to avoid losing the
history of the evolution, but they shouldn’t have any use for the
end user.
A (n_components, n_components) is the dictionary covariance matrix.
B (n_features, n_components) is the data approximation matrix</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations run.</p>
</dd>
<dt><strong>iter_offset_</strong><span class="classifier">int</span></dt><dd><p>The number of iteration on data batches that has been
performed before.</p>
</dd>
<dt><strong>random_state_</strong><span class="classifier">RandomState</span></dt><dd><p>RandomState instance that is generated either from a seed, the random
number generattor or by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.decomposition.SparseCoder.html#sklearn.decomposition.SparseCoder" title="sklearn.decomposition.SparseCoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparseCoder</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DictionaryLearning</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA" title="sklearn.decomposition.SparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparsePCA</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MiniBatchSparsePCA</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p><strong>References:</strong></p>
<p>J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning
for sparse coding (<a class="reference external" href="https://www.di.ens.fr/sierra/pdfs/icml09.pdf">https://www.di.ens.fr/sierra/pdfs/icml09.pdf</a>)</p>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit" title="sklearn.decomposition.MiniBatchDictionaryLearning.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit the model from data in X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform" title="sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_params" title="sklearn.decomposition.MiniBatchDictionaryLearning.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit" title="sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partial_fit</span></code></a>(self, X[, y, iter_offset])</p></td>
<td><p>Updates the model using the data in X as a mini-batch.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_params" title="sklearn.decomposition.MiniBatchDictionaryLearning.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.decomposition.MiniBatchDictionaryLearning.transform" title="sklearn.decomposition.MiniBatchDictionaryLearning.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Encode the data as a sparse combination of the dictionary atoms.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_components=None</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">n_iter=1000</em>, <em class="sig-param">fit_algorithm='lars'</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">batch_size=3</em>, <em class="sig-param">shuffle=True</em>, <em class="sig-param">dict_init=None</em>, <em class="sig-param">transform_algorithm='omp'</em>, <em class="sig-param">transform_n_nonzero_coefs=None</em>, <em class="sig-param">transform_alpha=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">split_sign=False</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">positive_code=False</em>, <em class="sig-param">positive_dict=False</em>, <em class="sig-param">transform_max_iter=1000</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1386"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1409"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model from data in X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training vector, where n_samples in the number of samples
and n_features is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the instance itself.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit">
<code class="sig-name descname">partial_fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">iter_offset=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1447"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the model using the data in X as a mini-batch.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training vector, where n_samples in the number of samples
and n_features is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd></dd>
<dt><strong>iter_offset</strong><span class="classifier">integer, optional</span></dt><dd><p>The number of iteration on data batches that has been
performed before this call to partial_fit. This is optional:
if no number is passed, the memory of the object is
used.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the instance itself.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.MiniBatchDictionaryLearning.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L896"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode the data as a sparse combination of the dictionary atoms.</p>
<p>Coding method is determined by the object parameter
<code class="docutils literal notranslate"><span class="pre">transform_algorithm</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape (n_samples, n_features)</span></dt><dd><p>Test data to be transformed, must have the same number of
features as the data used to train the model.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">array, shape (n_samples, n_components)</span></dt><dd><p>Transformed data</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-decomposition-minibatchdictionarylearning">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.decomposition.MiniBatchDictionaryLearning</span></code><a class="headerlink" href="#examples-using-sklearn-decomposition-minibatchdictionarylearning" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_image_denoising_thumb.png" src="../../_images/sphx_glr_plot_image_denoising_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" src="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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